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Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

 | 
Felice Dell'Orletta
, 
Johanna Monti
, 
Fabio Tamburini

Contributed Papers

Analysis of lexical semantic changes in corpora with the Diachronic Engine

Pierluigi Cassotti, Pierpaolo Basile, Marco De Gemmis et Giovanni Semeraro

Résumé

With the growing availability of digitized diachronic corpora, the need for tools capable of taking into account the diachronic component of corpora becomes ever more pressing. Recent works on diachronic embeddings show that computational approaches to the diachronic analysis of language seem to be promising, but they are not user friendly for people without a technical background. This paper presents the Diachronic Engine, a system for the diachronic analysis of corpora lexical features. Diachronic Engine computes word frequency, concordances and collocations taking into account the temporal dimension. It is also able to compute temporal word embeddings and time-series that can be exploited for lexical semantic change detection.

Texte intégral

The authors would like to thank Dr. Ferrante and Dr. Lopatriello for supporting the preliminary development of the Diachronic Engine (Ferrante 2019; Lopatriello 2020). This research has been partially funded by ADISU Puglia under the post-graduate programme “Emotional city: a location-aware sentiment analysis platform for mining citizen opinions and monitoring the perception of quality of life”.

1. Motivation and Background

1Synchronic corpora are widely used in linguistics for deriving a set of abstract rules that govern a particular language under analysis by using statistical approaches. The same methodology can be adopted for analyzing the evolution of word meanings over time in the case of diachronic corpora. However, this process can be very time-consuming. Usually, linguists rely on software tools that can easily explore and clean the corpus, while highlighting the more relevant linguistic features. Sketch Engine1 (Kilgarriff et al. 2004, 2014) is the leading tool in the corpus analysis field. Beyond several interesting features, Sketch Engine includes trends (Kilgarriff et al. 2015), which allow for diachronic analysis based on the frequency distribution of words. Trends rely on merely frequency features, ignoring word usage information. Moreover, the Sketch Engine interface does not provide temporal information about concordances and collocations. NoSketchEngine2 is an open-source version of SketchEngine. It requires technical expertise for the setup and, contrarily to SketchEngine, it does not support word sketches, terminology, thesaurus, n-grams, trends and corpus building. An interesting system is DiaCollo3 (Jurish and Wissenschaften 2015), a software tool for the discovery, comparison, and interactive visualization of target word combinations. Combinations can be requested for a particular time period, or for a direct comparison between different time periods. However, DiaCollo is focused exclusively on the extraction and visualization of collocations from diachronic corpora.

2In recent works about computational diachronic linguistics, techniques based on word embeddings produce promising results. In Semeval Task 1 (Schlechtweg et al. 2020), for instance, type embeddings rich high performances on both subtasks. However, these techniques are not included in any aforementioned linguistic tool. In order to bridge this gap, we try to build a tool that includes approaches for the analysis of diachronic embeddings. The result of our work is Diachronic Engine (DE), an engine for the management of diachronic corpora that provides tools for change detection of lexical semantics from a frequentist perspective. DE includes tools for extracting diachronic collocations, concordances in different time periods as well as for computing semantic change time-series by exploiting both word frequencies and word embeddings similarity over time.

3The rest of the paper is organized as follows: Section 2 describes the technical details of DE, while Section 3 shows some use cases of our engine that encompass that address time-series. We also present the results of a preliminary evaluation about the system’s usability in Section 4. Conclusions and future work close the paper.

2. Diachronic Engine

Figure 1: Diachronic Engine web interface

Image 10000000000003B0000001D141B04EDCE452BF4D.jpg

4Diachronic Engine (DE) is a web application for lexical semantic change analysis in diachronic corpora. The DE pipeline needs diachronic corpora to compute statistics about the corpus. A diachronic corpus must include a temporal feature (e.g., year or timestamp of the publication date); DE exploits that feature to sort the documents.

5We adopt the vertical format to represent word information, as specified for the IMS Corpus Workbench (CWB). In a vertical corpus, each word is in a new line. In each line, fields, called p-attributes, are separated by tabs. In DE the default p-attributes are word, lemma, PoS tag and syntactic dependency. Non-recursive XML tags (s-attributes) on a separate line can be used for representing sentences, paragraphs and documents.

6Corpora can be served in vertical format4 or in plain-text mode; in the latter case, the plain-text is transformed in vertical format using the Spacy UDPipe5 (Straka 2018) tool, which splits plain-text into sentences and then predicts the PoS-tag, the lemma and the syntactic dependency for each token. UDPipe is a dependency parser that provides models for several languages. Models are built by using the Universal Dependencies6 datasets as training data. Input files’ names must contain the temporal tag of the period to which they refer. DE automatically detects temporal patterns in the name of the files. In particular, the last sequence of numbers in the file name is used to sort the documents.

7Corpora are stored and managed by the CWB, a tool for the manipulation of large, linguistically annotated corpora. In particular, DE relies on the Corpus Query Processor (CQP) (Christ et al. 1999), a specialized search engine for linguistic research.

For building temporal word embeddings, DE exploits Temporal Random Indexing (TRI) (Basile, Caputo, and Semeraro 2014; Basile et al. 2016) that computes a word vector for each time period by summing shared random vectors over all the periods. TRI is able to produce aligned word embeddings in a single step and it is based on Random Indexing (Sahlgren 2005), where a word vector (word embedding) Image 1000000000000020000000197EFB037AAD53BAD0.jpgfor the word wj at time Tk is the sum of random vectors ri assigned to the co-occurring words taking into account only documents Image 100000000000003600000010B5505D5C252F46A9.jpg. Co-occurring words are defined as the set of m words that precede and follow the word wj. Random vectors are vectors initialized randomly and shared across all time slices so that word spaces are comparable.

8Future versions will include other approaches, such as Procustes (Hamilton, Leskovec, and Jurafsky 2016), Dynamic Word Embeddings (Yao et al. 2018), Dynamic Bernoulli Embeddings (Rudolph and Blei 2018) and Temporal Referencing (Dubossarsky et al. 2019).

9The DE architecture is based on the client-server paradigm. The back-end of DE has been developed with Flask, a web framework written in Python. Concordances are retrieved by CQP, that indexes the corpus as soon as it is uploaded to the server, while collocations and frequencies are computed in Python. The back-end provides a set of services by a REST API where the input/output is based on JSON messages.

10The back-end consists of three macro components: User Handler, Corpus Handler and Diachronic Operations. The User Handler manages registered users information such as username and passwords. Admitted operations on users are creation, read, update and delete. The Corpus Handler Component manages corpora information such as name, language, the list of fields in the vertical files, corpus visibility. Moreover, it deals with corpora types: each corpus has a label indicating if it is synchronic or diachronic. For diachronic corpora also the temporal range is stored. Operations admitted on corpora are: creation, update, delete, search and read. The Diachronic Operations component shows frequency lists, collocations of words, time-series, change-points and concordances. This component relies on CWB that indexes vertical files.

11The Diachronic Operations component architecture is sketched in Figure 2.

Figure 2: Diachronic Engine corpora manager

Image 10000000000003B0000001C1F2A008DA3B83C271.jpg

12The front-end of DE has been developed with JHipster7, using Spring8 for server-side applications and Angular for client-side applications. The front-end communicates with the back-end by the means of the REST API.

13The front-end design is inspired by the Google’s Material Design and the Sketch Engine interface. The user interface provides multilingual support in Italian and English, but we plan to extend it to other languages.

14This architecture allows the independence between the back-end and the front-end, in this way is possible to develop a different front-end or connect the front-end to a different implementation of the back-end. The only constraint is the REST API interface.

15A screenshot of the DE homepage is provided in Figure 1. The homepage provides an easy access to all corpora owned by the logged user with links to available tools. The front-end provides also tools for creating and managing users and corpora. In particular, it is possible to define different grant permissions for each corpus.

16The tool is distributed as open-source software under the GNU v3 license9.

2.1 DE tools

17DE provides a set of tools for managing and querying diachronic corpora. The core of the back-end is based on the IMS Open Corpus Workbench (CWB),10 which allows querying the indexed corpora by using the powerful CQP. Other tools have been integrated to facilitate the analysis of a diachronic corpus:

Word frequency

18Many works show a correlation between lexical semantic change and frequency differences between time periods. Google Ngram Viewer (Michel et al. 2011) uses n-grams frequencies over time to show the change in the semantics of n-grams. SketchEngine exposes the Trends tool, which uses a linear regression of frequencies to predict words that appear to be changed. In DE, queries can be filtered by part-of-speech, as well as by time periods. We use normalized frequencies, that can be filtered by time period.

Collocations

19Collocations have shown to be an effective tool in diachronic analysis (Basile, Semeraro, and Caputo 2019). A collocation is a sequence of words that occurs more often than would be expected. In order to compute the collocation strength we use the logDice (Rychlỳ 2008):

Image 10000000000000520000002C029C7B6CFE5AEF0D.jpg

logDice takes into account the frequency of the word fx, of the collocate fy and the frequency of the whole collocation fxy. Collocation results can be grouped by the PoS tag.

Concordances

20Concordances offer a way to find “the evidence” directly in the text by exploiting the context. The Concordances tool lists instances of a word with its immediate left and right context and the period the collocation belongs to. An example of concordances from “L’Unità” (Basile et al. 2020), is shown in Figure 3.

Figure 3: DE shows the KWIC (Keyword in the context) “pilotato”, shifted from meaning driven to meaning manipulated

Image 10000000000003B000000205CAAE43E993749B10.jpg

Time-series

A time-series Image 1000000000000026000000132B4C3502D4F82B92.jpgof a word w is an ordered sequence of cosine similarities between the word vector at time k(Image 10000000000000120000001552EEA7D2EA8D8E57.jpg) and the previous one at time k-1 (Image 100000000000002100000015CEE09B0974B41682.jpg):

Image 10000000000000910000002DC77320E571CF58F3.jpg

21Diachronic Engine relies on word vectors computed by Temporal Random Indexing, but it is possible to integrate other approaches. In order to detect change points, we use the Mean Shift algorithm (Taylor 2000). According to this model, we define a mean shift of a general time series Γ pivoted at time period j as:

Image 1000000000000100000000397C38B0977DE09BC8.jpg       (1)

22In order to understand if a mean shift is statistically significant at time j, a bootstrapping (Efron and Tibshirani 1994) approach under the null hypothesis that there is no change in the mean is adopted. In particular, statistical significance is computed by first constructing B bootstrap samples by permuting Γ(ti). Second, for each bootstrap sample P, K(P) is calculated to provide its corresponding bootstrap statistic and statistical significance (p-value) of observing the mean shift at time j compared to the null distribution. Finally, we estimate the change point by considering the time point j with the minimum p-value score. The output of this process is a ranking of words that potentially have changed meaning. Time-series is able to compare multiple words at the same time and allows to filter words by time period.

3. Use cases

23In this section, we describe two use cases concerning both historical and computational linguistics. DE is an extension of existing tools for synchronic corpora. It shares many of the use cases already available on those tools, such as applications in lexicography, terminology and linguistics.

Figure 4: DE shows time-series of the word “terrorismo”

Image 10000000000003B00000021BC1A745975792C686.jpg

3.1 Event detection through time-series

24Lexical semantic changes can reveal aspects of real-world events, such us global armed conflicts (Kutuzov, Velldal, and Øvrelid 2017). DE provides several tools to help events detection through time-series:

  • the comparison of two time-series for highlighting potential correlations between lexical-semantic changes

  • the plot of the time-series of cosine similarity between two word vectors over time, showing how the relatedness between two words changes over time

  • the detected change points can bring out hidden information

25In Figure 4, the time-series of “terrorismo” (terrorism) is shown. The time-series appears to be influenced by real-world events happening in Italy. In particular, we can observe a decrease in similarity starting in 1968 and culminating in 1970 during a crucial moment in Italy: “Anni di piombo” (Years of Lead), years marked by terrorism and violent clashes carried out by political activists.

3.2 Annotation of semantic shifts

26The manual annotation of lexical-semantic shifts can be very expensive. Although robust frameworks (Schlechtweg, Walde, and Eckmann 2018) for the annotations already exist and are successfully used in evaluation tasks (Schlechtweg et al. 2020), no tools for facilitating the annotation are available yet.

27DE can provide useful tools for the annotation of semantic shifts:

  1. Frequencies over time can be preliminary exploited to filter words that have good coverage in the years under analysis;

  2. Change points in time-series offer an overall and intuitive idea of the potential semantic shifts;

  3. Diachronic concordances and collocations can support the identification of the type of change (Blank 2012), such as when a word gains or loses a meaning.

4. Evaluation

28We place a particular focus on the usability of our tool by giving a satisfactory experience. To understand the strength and weakness of the user interface, we conduct a preliminary usability test, according to the eGLU protocol (Simone et al. 2015). We use 21 participants. As a first step of the evaluation, we want to test the system’s usability by measuring the task success rate: the ratio of users able to accomplish a set of predefined tasks. We ask participants to perform four tasks and we compute the average task success over all the 21 participants. During the evaluation, all participants complete their tasks without difficulties except for the showing frequency list task, where they had some problems with the corpus selection. We have already fixed this issue: the user is warned to choose a corpus from those available if no corpus is selected.

29Results of the evaluation are reported in Table 1.

Table 1: Results of the usability evaluation

Task

Avg. task success

User registration

1

Login and show user information

1

Add a corpus

1

Show frequency list

.8095

Overall

.9523

30Moreover, we designed and dispensed a questionnaire for measuring user satisfaction. The questionnaire is composed of ten questions about the usability and the design of DE with a Likert scale of five values. The questionnaire results return an average score of 84.05/100. The system appear likeable to use.

5. Conclusions

31In this paper, we present the Diachronic Engine, a tool for the analysis of lexical semantic change. DE integrates and extends current tools for corpus analysis enabling the study of corpus diachronic features. DE includes tools not included in other systems, such as time-series and change points detection based on diachronic word embeddings.

32As future work, we plan to provide pre-loaded corpora such as Google Ngram, Diacoris (Onelli et al. 2006) and the integration of other approaches for computing diachronic word embeddings. Moreover, we plan to add a tool for the annotation of lexical-semantic shifts inspired by DUREL (Schlechtweg, Walde, and Eckmann 2018).

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